apply_scaling

Interpretable nonparametric modeling of longitudinal data
using additive Gaussian process regression. Contains functionality
for inferring covariate effects and assessing covariate relevances.
Models are specified using a convenient formula syntax, and can include
shared, group-specific, non-stationary, heterogeneous and temporally
uncertain effects. Bayesian inference for model parameters is performed
using 'Stan'. The modeling approach and methods are described in detail in
Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.

Juho Timonen

lgpr

Longitudinal Gaussian Process Regression

Andrew Johnson

apply_scaling function

<dl><dt>scaling</dt>
<dd>an object of class lgpscaling</dd>
<dt>x</dt>
<dd>object to which apply the scaling (numeric)</dd>
<dt>inverse</dt>
<dd>whether scaling should be done in inverse direction</dd></dl>

Arguments

Apply variable scaling — apply_scaling

<dl>

<dt>scaling</dt>
<dd>an object of class lgpscaling</dd>


<dt>x</dt>
<dd>object to which apply the scaling (numeric)</dd>


<dt>inverse</dt>
<dd>whether scaling should be done in inverse direction</dd>

</dl>

apply_scaling: Apply variable scaling

Description

Usage

Value

Arguments

See Also